
#importing libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from pandas_profiling import ProfileReport
%matplotlib inline


# importing dataset into environment

tz = pd.read_csv('train(1).csv')
tz


# lets check out the shape of the dataset
tz.shape

(4809, 23)


tz.columns

Index(['ID', 'country', 'age_group', 'travel_with', 'total_female',
       'total_male', 'purpose', 'main_activity', 'info_source',
       'tour_arrangement', 'package_transport_int', 'package_accomodation',
       'package_food', 'package_transport_tz', 'package_sightseeing',
       'package_guided_tour', 'package_insurance', 'night_mainland',
       'night_zanzibar', 'payment_mode', 'first_trip_tz', 'most_impressing',
       'total_cost'],
      dtype='object')


tz.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4809 entries, 0 to 4808
Data columns (total 23 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   ID                     4809 non-null   object 
 1   country                4809 non-null   object 
 2   age_group              4809 non-null   object 
 3   travel_with            3695 non-null   object 
 4   total_female           4806 non-null   float64
 5   total_male             4804 non-null   float64
 6   purpose                4809 non-null   object 
 7   main_activity          4809 non-null   object 
 8   info_source            4809 non-null   object 
 9   tour_arrangement       4809 non-null   object 
 10  package_transport_int  4809 non-null   object 
 11  package_accomodation   4809 non-null   object 
 12  package_food           4809 non-null   object 
 13  package_transport_tz   4809 non-null   object 
 14  package_sightseeing    4809 non-null   object 
 15  package_guided_tour    4809 non-null   object 
 16  package_insurance      4809 non-null   object 
 17  night_mainland         4809 non-null   float64
 18  night_zanzibar         4809 non-null   float64
 19  payment_mode           4809 non-null   object 
 20  first_trip_tz          4809 non-null   object 
 21  most_impressing        4496 non-null   object 
 22  total_cost             4809 non-null   float64
dtypes: float64(5), object(18)
memory usage: 864.2+ KB


# checking duplicated data
tz.duplicated().sum()

0


# lets check for NaN values in the dataset
tz.isnull().sum()

ID                          0
country                     0
age_group                   0
travel_with              1114
total_female                3
total_male                  5
purpose                     0
main_activity               0
info_source                 0
tour_arrangement            0
package_transport_int       0
package_accomodation        0
package_food                0
package_transport_tz        0
package_sightseeing         0
package_guided_tour         0
package_insurance           0
night_mainland              0
night_zanzibar              0
payment_mode                0
first_trip_tz               0
most_impressing           313
total_cost                  0
dtype: int64


# lets see the NaN 
tz[['travel_with', 'total_female', 'total_male', 'most_impressing']]


# For total_female

null = tz['total_female'].isnull()
total_female_null = tz.loc[null == True]
total_female_null


# For total_male

null = tz['total_male'].isnull()
total_male_null = tz.loc[null == True]
total_male_null


# For most_impressing

null = tz['most_impressing'].isnull()
most_impressing_null = tz.loc[null == True]
most_impressing_null.head()


# For travel_with

null = tz['travel_with'].isnull()
travel_with_null = tz.loc[null == True]
travel_with_null.head()


tz.describe().T


# filling NaN. Datas that are string are filled using ffill method and float datas are filled using bfill method.
tz['travel_with'].fillna(method = 'ffill', inplace = True)
tz['total_female'].fillna(method = 'bfill', inplace = True)
tz['total_male'].fillna(method = 'bfill', inplace = True)
tz['most_impressing'].fillna(method = 'ffill', inplace = True)


tz.head()


# chhecking for NaN after filling NaN

tz.isnull().sum()

ID                       0
country                  0
age_group                0
travel_with              0
total_female             0
total_male               0
purpose                  0
main_activity            0
info_source              0
tour_arrangement         0
package_transport_int    0
package_accomodation     0
package_food             0
package_transport_tz     0
package_sightseeing      0
package_guided_tour      0
package_insurance        0
night_mainland           0
night_zanzibar           0
payment_mode             0
first_trip_tz            0
most_impressing          0
total_cost               0
dtype: int64


# checking for duplicated

tz.duplicated().sum()

0


# Lets describe the data after filling NaN
tz.describe().T


profile_report = ProfileReport(tz, title = 'pandas profiling  report', explorative = True)


profile_report


info_sources = sns.countplot(x = 'info_source', data = tz, color = '#42b7bd')
patch_h = [patch.get_height() for patch in info_sources.patches] 
idx_tallest = np.argmax(patch_h) 
info_sources.patches[idx_tallest].set_facecolor('#a834a8')
plt.xticks(rotation = 90)
plt.show()


tz.groupby(['info_source'])['total_cost'].sum().nlargest().plot(kind = 'pie', autopct = '%1.2f%%',  shadow = False)

<AxesSubplot:ylabel='total_cost'>


tz['total_female'].plot.hist(edgecolor = 'black', grid = False)
plt.title('histogram plot for total female')

Text(0.5, 1.0, 'histogram plot for total female')


sns.boxplot(x = 'total_female', data = tz)
plt.style.use('bmh')


tz['total_female'].plot.kde(linewidth = 1, figsize = (4,2))

<AxesSubplot:ylabel='Density'>


tz['total_male'].plot.hist(edgecolor = 'black', grid = False, figsize = (4,2))
plt.title('histogram plot for total male')
plt.show()


sns.boxplot(x = 'total_male', data =tz)

<AxesSubplot:xlabel='total_male'>


sns.kdeplot(x = 'total_male', linewidth = 1, data = tz)

<AxesSubplot:xlabel='total_male', ylabel='Density'>


tz['night_mainland'].plot.hist(edgecolor = 'black', bins = 50, grid = False,color = 'red', figsize = (4,2))
plt.title('histogram of night mainland')
plt.show()


tz['night_zanzibar'].plot.hist(edgecolor = 'black', bins = 50, grid = False, figsize = (4,2))
plt.title('histogram of night zanzibar')
plt.show()


tz['total_cost'].plot.hist(edgecolor = 'black', bins = 50, grid = False, figsize = (4,2))
plt.show()


sns.distplot(tz['total_cost'])

C:\Users\COMPUTER\anaconda3\lib\site-packages\seaborn\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:xlabel='total_cost', ylabel='Density'>


tz_country = tz.groupby('country')['total_cost'].sum().sort_values(ascending = False).head(6)
tz_country

country
UNITED STATES OF AMERICA    8.890832e+09
UNITED KINGDOM              3.808383e+09
ITALY                       3.762160e+09
FRANCE                      3.344496e+09
AUSTRALIA                   2.743132e+09
SOUTH AFRICA                2.594805e+09
Name: total_cost, dtype: float64


plt.style.use('seaborn')
tz_country.plot(figsize = (6,3), legend = False, kind = 'bar', colormap = 'winter')
plt.title('showing the top six countries with highest spending')
plt.show()


tz_age = tz.groupby('age_group')['total_cost'].sum().sort_values(ascending = False)
tz_age

age_group
45-64    1.537184e+10
25-44    1.498710e+10
65+      5.284068e+09
1-24     3.379088e+09
Name: total_cost, dtype: float64


tz_age.plot.bar(edgecolor = 'black', colormap='winter', grid = False, figsize = (4,2))
plt.title('spenders by age group ')
plt.xticks(rotation = 'horizontal')
plt.show()


tz['age_group'].value_counts()

25-44    2487
45-64    1391
1-24      624
65+       307
Name: age_group, dtype: int64


tz.value_counts('age_group').plot(kind = 'pie', autopct = '%1.2f%%')

<AxesSubplot:ylabel='None'>


sns.countplot(x = 'age_group', hue = 'travel_with', data = tz)
plt.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
plt.title('age group of tourist by travel with ')
plt.figure(figsize = (6,3))
plt.show()

<Figure size 432x216 with 0 Axes>


tz_age_group = tz.groupby(['age_group', 'travel_with'])['total_cost'].sum()
tz_age_group
# age goup with repect to travel with total spendings

age_group  travel_with        
1-24       Alone                  1.073558e+09
           Children               3.906452e+07
           Friends/Relatives      1.545524e+09
           Spouse                 4.267596e+08
           Spouse and Children    2.941818e+08
25-44      Alone                  2.125659e+09
           Children               4.997074e+08
           Friends/Relatives      4.793482e+09
           Spouse                 5.314020e+09
           Spouse and Children    2.254231e+09
45-64      Alone                  1.623682e+09
           Children               1.162442e+09
           Friends/Relatives      3.020189e+09
           Spouse                 5.042900e+09
           Spouse and Children    4.522627e+09
65+        Alone                  7.135170e+08
           Children               1.716295e+08
           Friends/Relatives      9.079032e+08
           Spouse                 3.283155e+09
           Spouse and Children    2.078632e+08
Name: total_cost, dtype: float64


tz_travel_with = tz.groupby('travel_with')['total_cost'].sum().sort_values(ascending = False)
tz_travel_with

travel_with
Spouse                 1.406684e+10
Friends/Relatives      1.026710e+10
Spouse and Children    7.278903e+09
Alone                  5.536416e+09
Children               1.872843e+09
Name: total_cost, dtype: float64


plt.style.use('seaborn')
tz_travel_with.plot(kind = 'bar', legend = False, colormap = 'winter', figsize = (6,3))
plt.title('total cost against travel with')
plt.show()


sns.barplot(y = 'total_cost', x = 'age_group',  hue = 'travel_with', data = tz)
plt.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
plt.title('Bar plot of total cost and age group of travel with tourist')
plt.show()


sns.countplot(x = 'payment_mode', data = tz)

<AxesSubplot:xlabel='payment_mode', ylabel='count'>


sns.countplot(x = 'main_activity', data = tz)
plt.xticks(rotation = 90)
plt.show()


# considering the purpose
sns.countplot(x = 'purpose', data = tz)
plt.xticks(rotation = 90, fontsize = 8 )
plt.title('purpose of visiting Tanzania')
plt.show()


sns.countplot( x = 'purpose', hue = 'main_activity', data = tz)
plt.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
plt.xticks (rotation =90)
plt.show()


sns.barplot(x = 'purpose', y = 'total_cost', hue = 'main_activity', data = tz)
plt.legend(loc = 'center left', bbox_to_anchor = (1,0.5))
plt.xticks (rotation =90)
plt.show()


tz['night_mainland'].mean()

8.488043252235393


tz['night_zanzibar'].mean()

2.30442919525889


sns.countplot(x = 'package_food', data = tz)
plt.show()


sns.countplot(x = 'package_food', hue = 'purpose', data = tz)
plt.legend(loc = 'center left', bbox_to_anchor = (1, 0.5))
plt.show()


sns.countplot(x = 'package_food', hue = 'main_activity', data = tz)
plt.legend(loc = 'center left', bbox_to_anchor = (1, 0.5))
plt.show()


tz.corr()


sns.heatmap(tz.corr(), annot = True)

<AxesSubplot:>


sns.pairplot(tz)

<seaborn.axisgrid.PairGrid at 0x15e590a1190>


# lets call out our data set 
tz.head() #missing data have been treated and fixed where necessary


tz.isnull().sum()

ID                       0
country                  0
age_group                0
travel_with              0
total_female             0
total_male               0
purpose                  0
main_activity            0
info_source              0
tour_arrangement         0
package_transport_int    0
package_accomodation     0
package_food             0
package_transport_tz     0
package_sightseeing      0
package_guided_tour      0
package_insurance        0
night_mainland           0
night_zanzibar           0
payment_mode             0
first_trip_tz            0
most_impressing          0
total_cost               0
dtype: int64


tz.dtypes.unique()

array([dtype('O'), dtype('float64')], dtype=object)


num = ['float64']
num_vars = list(tz.select_dtypes(include = num))
num_vars

['total_female',
 'total_male',
 'night_mainland',
 'night_zanzibar',
 'total_cost']


df = tz[['total_female',
 'total_male',
 'night_mainland',
 'night_zanzibar',
 'total_cost'
 ]]
df.head()


p = df.iloc[:, 0:4]
p


r = df['total_cost']


from sklearn.model_selection import train_test_split


x_train, x_test, y_train, y_test = train_test_split(p, r, test_size = 0.30, random_state = 0)


corrmatrix = x_train.corr()
corrmatrix


sns.heatmap(corrmatrix, annot = True)

<AxesSubplot:>


def correlation (df, threshold):
    correlated_cols = set()
    corr_matrix = df.corr()
    for i in range(len(corr_matrix.columns)):
        for j in range(i):
            if abs(corr_matrix.iloc[i,j]) > threshold:
                colname = corr_matrix.columns[i]
                correlated_cols.add(colname)
    return correlated_cols


correlation(x_train, 0.70)

set()


tz.columns

Index(['ID', 'country', 'age_group', 'travel_with', 'total_female',
       'total_male', 'purpose', 'main_activity', 'info_source',
       'tour_arrangement', 'package_transport_int', 'package_accomodation',
       'package_food', 'package_transport_tz', 'package_sightseeing',
       'package_guided_tour', 'package_insurance', 'night_mainland',
       'night_zanzibar', 'payment_mode', 'first_trip_tz', 'most_impressing',
       'total_cost'],
      dtype='object')


tz.head()


x = tz.drop(columns = [ 'country', 'payment_mode', 'ID', 'total_cost', 'most_impressing', 'info_source', 'first_trip_tz' ])
x


y = tz['total_cost']


# Encoding our data set for training
x = pd.get_dummies(x)
x


from sklearn.model_selection import train_test_split


x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 0.30, train_size = 0.70, random_state = 0)


from sklearn.preprocessing import MinMaxScaler


scaler = MinMaxScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.transform(x_test)


x_train

array([[0.06666667, 0.06666667, 0.03448276, ..., 0.        , 1.        ,
        0.        ],
       [0.        , 0.06666667, 0.00689655, ..., 0.        , 1.        ,
        0.        ],
       [0.06666667, 0.06666667, 0.        , ..., 0.        , 1.        ,
        0.        ],
       ...,
       [0.06666667, 0.        , 0.00689655, ..., 0.        , 1.        ,
        0.        ],
       [0.13333333, 0.06666667, 0.02758621, ..., 1.        , 1.        ,
        0.        ],
       [0.06666667, 0.        , 0.04137931, ..., 1.        , 1.        ,
        0.        ]])


from sklearn.linear_model import LinearRegression


model = LinearRegression()


model.fit(x_train, y_train)

LinearRegression()


model.intercept_

2.9079651349221954e+19


model.coef_

array([ 2.10219744e+07,  2.24233753e+07,  1.21003342e+07,  1.32624155e+07,
       -1.84360674e+18, -1.84360674e+18, -1.84360674e+18, -1.84360674e+18,
       -4.94179667e+19, -4.94179667e+19, -4.94179667e+19, -4.94179667e+19,
       -4.94179667e+19,  2.74729840e+19,  2.74729840e+19,  2.74729840e+19,
        2.74729840e+19,  2.74729840e+19,  2.74729840e+19,  2.74729840e+19,
       -1.22236968e+19, -1.22236968e+19, -1.22236968e+19, -1.22236968e+19,
       -1.22236968e+19, -1.22236968e+19, -1.22236968e+19, -1.22236968e+19,
       -1.22236968e+19,  1.16219654e+18,  1.16219654e+18, -1.90576969e+18,
       -1.90576969e+18, -8.00858477e+15, -8.00858477e+15,  2.52443411e+18,
        2.52443411e+18, -2.47831827e+16, -2.47831827e+16,  1.23975372e+18,
        1.23975372e+18,  1.97340877e+18,  1.97340877e+18,  1.97140321e+18,
        1.97140321e+18])


prediction = model.predict(x_test)
prediction

array([ 8544256., 15101952.,  4468736., ...,  9125888.,  3698688.,
        2174976.])


y_test

825       530400.0
1705    13842913.0
4133     1259900.0
3537    22376250.0
3555      411600.0
           ...    
2157     8494687.5
1300    14420250.0
2383     2734875.0
242       994500.0
3626       56628.0
Name: total_cost, Length: 1443, dtype: float64


from numpy import math
from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error


math.sqrt(mean_squared_error(y_test, prediction))

10245449.3440015


from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error


score = r2_score(y_test, prediction)
score

0.279164433125888


mae = mean_absolute_error(y_test, prediction)
mse = mean_squared_error(y_test, prediction)
rmse = np.sqrt(mse)

mae

5825199.193148994

mse

104969232260500.75


rmse

10245449.3440015


print(model.score(x_test,y_test))
print(model.score(x_train, y_train))

0.279164433125888
0.3561318991325134


# importing the test dataset
tz2 = pd.read_csv('Test .csv')
tz2.head()


tz2.columns

Index(['ID', 'country', 'age_group', 'travel_with', 'total_female',
       'total_male', 'purpose', 'main_activity', 'info_source',
       'tour_arrangement', 'package_transport_int', 'package_accomodation',
       'package_food', 'package_transport_tz', 'package_sightseeing',
       'package_guided_tour', 'package_insurance', 'night_mainland',
       'night_zanzibar', 'payment_mode', 'first_trip_tz', 'most_impressing'],
      dtype='object')


tz2.isna().sum()

ID                         0
country                    0
age_group                  0
travel_with              327
total_female               1
total_male                 2
purpose                    0
main_activity              0
info_source                0
tour_arrangement           0
package_transport_int      0
package_accomodation       0
package_food               0
package_transport_tz       0
package_sightseeing        0
package_guided_tour        0
package_insurance          0
night_mainland             0
night_zanzibar             0
payment_mode               0
first_trip_tz              0
most_impressing          111
dtype: int64


tz2['travel_with'].fillna(method = 'ffill', inplace = True)
tz2['most_impressing'].fillna(method = 'ffill', inplace = True)
tz2['total_female'].fillna(method = 'bfill', inplace = True)
tz2['total_male'].fillna(method = 'bfill', inplace = True)


tz2.age_group.value_counts()

25-44     866
45-64     447
24-Jan    202
65+        86
Name: age_group, dtype: int64


tz2.loc[(tz2['age_group'] == '24-Jan'), 'age_group'] = '1-24'


tz2.age_group.value_counts()

25-44    866
45-64    447
1-24     202
65+       86
Name: age_group, dtype: int64

tz2


z = tz2.drop(columns = ['ID', 'country', 'payment_mode', 'most_impressing', 'info_source', 'first_trip_tz'  ])
z


z = pd.get_dummies(z) #Encoding
z


model.fit(x, y)

LinearRegression()


predictions = model.predict(z)


predictions

array([18288618.03153989, 11058112.3520682 ,  7044555.01761863, ...,
       -1369307.94169107, -2354306.83891222,  3017495.87123583])


new_total_cost = tz2.assign(predicted_price = predictions)
new_total_cost


plt.scatter(y_test, prediction)

<matplotlib.collections.PathCollection at 0x15e55043df0>

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
1017	tour_2246	UNITED STATES OF AMERICA	45-64	Friends/Relatives	4.0	NaN	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	19.0	0.0	Cash	Yes	NaN	7293000.0
1473	tour_2810	SPAIN	45-64	Friends/Relatives	7.0	NaN	Other	Hunting tourism	Travel, agent, tour operator	Package Tour	...	Yes	No	No	Yes	11.0	0.0	Cash	No	Wonderful Country, Landscape, Nature	56960183.0
2449	tour_3980	SOUTH AFRICA	25-44	Friends/Relatives	0.0	NaN	Meetings and Conference	Wildlife tourism	Newspaper, magazines,brochures	Independent	...	No	No	No	No	4.0	0.0	Credit Card	Yes	Friendly People	3978000.0
2494	tour_4031	FRANCE	25-44	NaN	0.0	NaN	Leisure and Holidays	Conference tourism	Radio, TV, Web	Package Tour	...	Yes	Yes	Yes	Yes	6.0	0.0	Cash	Yes	Excellent Experience	4309500.0
3984	tour_5838	CHINA	25-44	Friends/Relatives	10.0	NaN	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	4.0	2.0	Cash	Yes	Wildlife	89505000.0

	count	mean	std	min	25%	50%	75%	max
total_female	4806.0	9.267582e-01	1.288242e+00	0.0	0.0	1.0	1.0	49.0
total_male	4804.0	1.009575e+00	1.138865e+00	0.0	1.0	1.0	1.0	44.0
night_mainland	4809.0	8.488043e+00	1.042762e+01	0.0	3.0	6.0	11.0	145.0
night_zanzibar	4809.0	2.304429e+00	4.227080e+00	0.0	0.0	0.0	4.0	61.0
total_cost	4809.0	8.114389e+06	1.222490e+07	49000.0	812175.0	3397875.0	9945000.0	99532875.0

	count	mean	std	min	25%	50%	75%	max
total_female	4809.0	9.270119e-01	1.288257e+00	0.0	0.0	1.0	1.0	49.0
total_male	4809.0	1.009773e+00	1.139276e+00	0.0	1.0	1.0	1.0	44.0
night_mainland	4809.0	8.488043e+00	1.042762e+01	0.0	3.0	6.0	11.0	145.0
night_zanzibar	4809.0	2.304429e+00	4.227080e+00	0.0	0.0	0.0	4.0	61.0
total_cost	4809.0	8.114389e+06	1.222490e+07	49000.0	812175.0	3397875.0	9945000.0	99532875.0

	total_female	total_male	night_mainland	night_zanzibar	total_cost
total_female	1.000000	0.467000	0.031233	0.138523	0.285862
total_male	0.467000	1.000000	-0.041369	0.050172	0.183785
night_mainland	0.031233	-0.041369	1.000000	-0.118155	0.020473
night_zanzibar	0.138523	0.050172	-0.118155	1.000000	0.145139
total_cost	0.285862	0.183785	0.020473	0.145139	1.000000

	total_female	total_male	night_mainland	night_zanzibar
total_female	1.000000	0.243339	0.048562	0.156284
total_male	0.243339	1.000000	-0.038897	0.059172
night_mainland	0.048562	-0.038897	1.000000	-0.112687
night_zanzibar	0.156284	0.059172	-0.112687	1.000000

CONTEXT¶

OBJECTIVE¶

IMPORTING LIBRARIES AND DATASET¶

DESCRIBING AND UNDERSTANDING OF DATASET¶

PROFILE REPORT¶

EXPLORATIVE DATA ANALYSIS¶

WHAT IS THE INFO SOURCE OF TOURIST?¶

TOTAL FEMALE¶

TOTAL MALE¶

NIGHT MAINLAND¶

NIGHT ZANZIBAR¶

TOTAL COST¶

What are the top 5 countries with the highest spending statistic ?¶

which age-group are the highest spenders and who are the over all highest spenders by travel with?¶

what is the most prefered payment mode by tourists?¶

Highlight the Aspects of tourism that are more profitable and in which it is worthwhile to invest in¶

what is the average number of nights a toursits spends in Tanzania mainland?¶

what is the average number of nights a toursits spends in Zanzibar?¶

what is the most sort after food by tourists?¶

HEATMAP¶

pairplot to explore the relationship between the numerical features in the dataset¶

Data Preprocessing¶

CHECKING MULTICOLLINEARITY¶

Building a Regression Model¶

Evaluate Model Performances¶

Testing Linear Model Assumptions¶

Actionable Insights and Recommendations¶

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	NaN	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	NaN	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	NaN	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
490	tour_1604	FRANCE	25-44	Friends/Relatives	NaN	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	9.0	5.0	Cash	Yes	No comments	313000.0
545	tour_1678	ITALY	1-24	Friends/Relatives	NaN	4.0	Volunteering	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	15.0	0.0	Cash	Yes	No comments	9077320.0
1261	tour_2547	FRANCE	25-44	Friends/Relatives	NaN	1.0	Leisure and Holidays	Wildlife tourism	Newspaper, magazines,brochures	Package Tour	...	Yes	Yes	Yes	Yes	7.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	324300.0

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
8	tour_101	NIGERIA	25-44	Alone	0.0	1.0	Leisure and Holidays	Cultural tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	4.0	0.0	Cash	Yes	NaN	994500.0
49	tour_1058	UAE	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	7.0	1.0	Cash	No	NaN	1657500.0
56	tour_1066	FRANCE	65+	Spouse	1.0	1.0	Visiting Friends and Relatives	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	56.0	0.0	Cash	Yes	NaN	2269330.0
64	tour_1074	FRANCE	25-44	Alone	0.0	1.0	Leisure and Holidays	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	3.0	0.0	Cash	Yes	NaN	400000.0
82	tour_1098	SOUTH AFRICA	25-44	Friends/Relatives	0.0	2.0	Meetings and Conference	Mountain climbing	Friends, relatives	Independent	...	No	No	No	No	1.0	0.0	Cash	No	NaN	414375.0

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_food	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing
0	tour_1	AUSTRALIA	45-64	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	Yes	10	3	Cash	Yes	Wildlife
1	tour_100	SOUTH AFRICA	25-44	Friends/Relatives	0.0	4.0	Business	Wildlife tourism	Tanzania Mission Abroad	Package Tour	...	No	No	No	No	No	13	0	Cash	No	Wonderful Country, Landscape, Nature
2	tour_1001	GERMANY	25-44	Friends/Relatives	3.0	0.0	Leisure and Holidays	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	No	7	14	Cash	No	No comments
3	tour_1006	CANADA	24-Jan	Friends/Relatives	2.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	No	0	4	Cash	Yes	Friendly People
4	tour_1009	UNITED KINGDOM	45-64	Friends/Relatives	2.0	2.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Package Tour	...	Yes	Yes	No	No	No	10	0	Cash	Yes	Friendly People

	total_female	total_male	night_mainland	night_zanzibar	age_group_1-24	age_group_25-44	age_group_45-64	age_group_65+	travel_with_Alone	travel_with_Children	...	package_food_No	package_food_Yes	package_transport_tz_No	package_transport_tz_Yes	package_sightseeing_No	package_sightseeing_Yes	package_guided_tour_No	package_guided_tour_Yes	package_insurance_No	package_insurance_Yes
0	1.0	1.0	10	3	0	0	1	0	0	0	...	0	1	0	1	0	1	0	1	0	1
1	0.0	4.0	13	0	0	1	0	0	0	0	...	1	0	1	0	1	0	1	0	1	0
2	3.0	0.0	7	14	0	1	0	0	0	0	...	1	0	1	0	1	0	1	0	1	0
3	2.0	0.0	0	4	1	0	0	0	0	0	...	1	0	1	0	1	0	1	0	1	0
4	2.0	2.0	10	0	0	0	1	0	0	0	...	0	1	0	1	1	0	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1596	0.0	1.0	1	0	0	1	0	0	0	0	...	1	0	1	0	1	0	1	0	1	0
1597	3.0	1.0	10	5	0	0	1	0	0	0	...	0	1	0	1	0	1	1	0	1	0
1598	0.0	1.0	6	0	0	1	0	0	1	0	...	1	0	1	0	1	0	1	0	1	0
1599	0.0	1.0	4	0	1	0	0	0	1	0	...	1	0	1	0	1	0	1	0	1	0
1600	1.0	1.0	9	5	0	1	0	0	0	0	...	1	0	1	0	1	0	1	0	1	0